RevisionStats.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "import data_loader\n",
    "import pandas as pd\n",
    "from matplotlib_venn import venn2\n",
    "import matplotlib\n",
    "# matplotlib font parameters\n",
    "font = {'family' : 'sans-serif',\n",
    "        'sans-serif':['Arial'],\n",
    "        'weight' : 'normal',\n",
    "        'size'   : 16}\n",
    "\n",
    "matplotlib.rc('font', **font)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "peptides = data_loader.Peptide()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "peptides.pacbio_filtered_peptide\n",
    "\n",
    "ms_accessions = set()\n",
    "for accs in peptides.pacbio_filtered_peptide['accs']:\n",
    "    ms_accessions = ms_accessions.union(accs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "structural_category      found_in_ms\n",
       "full-splice_match        False           6504\n",
       "                         True            6980\n",
       "incomplete-splice_match  False           4765\n",
       "                         True             786\n",
       "novel_in_catalog         False          10640\n",
       "                         True            3527\n",
       "novel_not_in_catalog     False           8261\n",
       "                         True            3605\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pclass = pd.read_table('/Users/bj8th/Documents/Sheynkman-Lab/Data/JURKAT_06-06-2021/protein_filter/jurkat.classification_filtered.tsv', usecols=['pb','pr_gene', 'CPM', 'pclass', 'psubclass'])\n",
    "tclass = pd.read_table('/Users/bj8th/Documents/Sheynkman-Lab/Data/JURKAT_06-06-2021/sqanti3-filtered/jurkat_classification.5degfilter.tsv', usecols=['isoform', 'structural_category'])\n",
    "classification = pclass.merge(tclass, how='left', left_on='pb', right_on='isoform')\n",
    "classification['found_in_ms'] = classification['pb'].isin(ms_accessions)\n",
    "\n",
    "\n",
    "\n",
    "classification.groupby(['structural_category', 'found_in_ms']).size()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pclass  found_in_ms\n",
       "pFSM    False           7953\n",
       "        True            8378\n",
       "pNIC    False           5376\n",
       "        True            2266\n",
       "pNNC    False          16841\n",
       "        True            4254\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classification.groupby(['pclass', 'found_in_ms']).size()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PacBio unique\t1560\n",
      "GENCODE unique\t7054\n",
      "Intersection\t69201\n",
      "0.9074945905186546\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib_venn._common.VennDiagram at 0x7f9451696790>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "filtered_peptide_set= set(peptides.pacbio_filtered_peptide['Full Sequence'])\n",
    "gencode_peptide_set = set(peptides.gencode_peptide['Full Sequence'])\n",
    "import matplotlib.pyplot as plt\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "print(f'PacBio unique\\t{len(filtered_peptide_set - gencode_peptide_set)}')\n",
    "print(f'GENCODE unique\\t{len(gencode_peptide_set - filtered_peptide_set)}')\n",
    "print(f'Intersection\\t{len(filtered_peptide_set.intersection(gencode_peptide_set))}')\n",
    "print(len(filtered_peptide_set.intersection(gencode_peptide_set)) / len(gencode_peptide_set))\n",
    "venn2([filtered_peptide_set, gencode_peptide_set], set_labels=['PacBio', 'GENCODE'], ax=ax)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mass_spec_venn import make_venn_diagram\n",
    "import matplotlib.pyplot as plt\n",
    "short_long_comparision = {\n",
    "    'PacBio-Hybrid (Sampling Space)': 3796, \n",
    "    'Overlap':43915, \n",
    "    'StringTie (Sampling Space)': 1316\n",
    "}\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(5,5),tight_layout=True)\n",
    "vd = venn2(\n",
    "    subsets = (short_long_comparision['PacBio-Hybrid (Sampling Space)'], short_long_comparision['StringTie (Sampling Space)'], short_long_comparision['Overlap']), \n",
    "    set_colors=['darkred' , 'gold'], \n",
    "    set_labels=None,\n",
    "    alpha=1, \n",
    "    ax=ax\n",
    ")\n",
    "\n",
    "h,l = [],[]\n",
    "\n",
    "vd.get_patch_by_id('11').set_color('lightgray')\n",
    "left_label = 'PacBio-Hybrid'\n",
    "right_label='StringTie'\n",
    "total = sum(short_long_comparision.values())\n",
    "for i in ['10','11','01']:\n",
    "    num = int(vd.get_label_by_id(i).get_text())\n",
    "    lbl = vd.get_label_by_id(i)\n",
    "    lbl.set_text(f'{num}\\n{num/total:.1%}')\n",
    "    x, y = lbl.get_position()\n",
    "    if i == '10':\n",
    "        lbl.set_position((x-0.2, y))\n",
    "    if i == '01':\n",
    "        lbl.set_position((x+0.2, y))\n",
    "    h.append(vd.get_patch_by_id(i))\n",
    "vd.get_patch_by_id('11').set_color('lightgray')\n",
    "l = [left_label, \"Both\", right_label]\n",
    "legend_xpos = 0.92\n",
    "ax.legend(handles=h,labels=l,title='Database',loc='best', bbox_to_anchor=(legend_xpos, -0.))\n",
    "plt.savefig('PacBio_StringTie_venn.svg', bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "score_distinct_ifile = 'Score_PEP_for distinctPeptides.txt'\n",
    "scores = pd.read_table(score_distinct_ifile)\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "import seaborn as sns \n",
    "\n",
    "# sns.kdeplot(x=scores['Hybrid Only Peptide Score'])\n",
    "bins=25\n",
    "scores['Hybrid Only Peptide Score'].hist(bins=bins, alpha=0.5, color='darkred', ax=ax, label='PacBio Hybrid Only')\n",
    "scores['StringTie Only Peptide Score'].hist(bins=bins, alpha=0.5, color='gold', ax=ax, label='StringTie Only')\n",
    "ax.legend()\n",
    "ax.grid(False)\n",
    "ax.set_xlabel('Peptide score')\n",
    "ax.set_ylabel('Count')\n",
    "plt.savefig('Hybrid_StringTie_peptide_score.svg', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "bins=25\n",
    "fig, ax = plt.subplots()\n",
    "scores['-log10(Hybrid PEP)'] = -np.log10(scores['Hybrid Only Peptide PEP'] + 1e-8)\n",
    "scores['-log10(StringTie PEP)'] = -np.log10(scores['StringTie Only Peptide PEP'] + 1e-8)\n",
    "\n",
    "scores['-log10(Hybrid PEP)'].hist(bins=bins, alpha=0.5, color='darkred', ax=ax, label='PacBio Only')\n",
    "scores['-log10(StringTie PEP)'].hist(bins=bins, alpha=0.5, color='gold', ax=ax, label='StringTie Only')\n",
    "ax.legend()\n",
    "ax.grid(False)\n",
    "ax.set_xlabel('$-log_{10}(PEP\\ QValue)$')\n",
    "ax.set_ylabel('Count')\n",
    "plt.savefig('Hybrid_StringTie_peptide_PEP.svg', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Hybrid Only Peptide Score</th>\n",
       "      <th>StringTie Only Peptide Score</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "      <th>Hybrid Only Peptide PEP</th>\n",
       "      <th>StringTie Only Peptide PEP</th>\n",
       "      <th>-log10(Hybrid PEP)</th>\n",
       "      <th>-log10(StringTie PEP)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>37.269</td>\n",
       "      <td>37.226</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000078</td>\n",
       "      <td>0.000138</td>\n",
       "      <td>4.107905</td>\n",
       "      <td>3.860168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>36.233</td>\n",
       "      <td>35.292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>4.621602</td>\n",
       "      <td>5.261219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>36.217</td>\n",
       "      <td>34.323</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.000033</td>\n",
       "      <td>4.795880</td>\n",
       "      <td>4.482804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.350</td>\n",
       "      <td>33.362</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000195</td>\n",
       "      <td>0.000053</td>\n",
       "      <td>3.710304</td>\n",
       "      <td>4.271646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.242</td>\n",
       "      <td>33.282</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>5.233587</td>\n",
       "      <td>5.060481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3791</th>\n",
       "      <td>8.137</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.006631</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.178432</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3792</th>\n",
       "      <td>8.137</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000763</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.117476</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3793</th>\n",
       "      <td>8.136</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.241353</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.617347</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3794</th>\n",
       "      <td>8.135</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.009306</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.031259</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3795</th>\n",
       "      <td>8.134</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.004796</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.319091</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3796 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Hybrid Only Peptide Score  ...  -log10(StringTie PEP)\n",
       "0                        37.269  ...               3.860168\n",
       "1                        36.233  ...               5.261219\n",
       "2                        36.217  ...               4.482804\n",
       "3                        35.350  ...               4.271646\n",
       "4                        35.242  ...               5.060481\n",
       "...                         ...  ...                    ...\n",
       "3791                      8.137  ...                    NaN\n",
       "3792                      8.137  ...                    NaN\n",
       "3793                      8.136  ...                    NaN\n",
       "3794                      8.135  ...                    NaN\n",
       "3795                      8.134  ...                    NaN\n",
       "\n",
       "[3796 rows x 7 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores"
   ]
  }
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